TY - GEN
T1 - Energy Aware Collaborative Machine Learning on Energy-Harvesting Devices
AU - Sun, Qi Hui
AU - Tu, Chia Heng
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Performing machine learning tasks on low end devices enables the development of various smart applications. Especially, these low end devices are often equipped with ultra-low-power microcontroller units (MCUs) that have weak computation power and few memory resources. It is a more challenging work to put these machine learning tasks on those end devices powered by harvested ambient energy, which are often referred to as energy-harvesting (EH) devices, since the unstable ambient energy can lead to the execution failure of the machine learning tasks. This paper proposes an adaptive energy-aware design to coordinate multiple EH devices to accomplish multi-class classification computation. It also leverages the concept of the One-vs-All (OVA) strategy turning a multi-class classification into multiple binary classifications. The experimental results show our work performs better than the widely used round-robin policy and self-greedy policy in consideration of time and energy consumption.
AB - Performing machine learning tasks on low end devices enables the development of various smart applications. Especially, these low end devices are often equipped with ultra-low-power microcontroller units (MCUs) that have weak computation power and few memory resources. It is a more challenging work to put these machine learning tasks on those end devices powered by harvested ambient energy, which are often referred to as energy-harvesting (EH) devices, since the unstable ambient energy can lead to the execution failure of the machine learning tasks. This paper proposes an adaptive energy-aware design to coordinate multiple EH devices to accomplish multi-class classification computation. It also leverages the concept of the One-vs-All (OVA) strategy turning a multi-class classification into multiple binary classifications. The experimental results show our work performs better than the widely used round-robin policy and self-greedy policy in consideration of time and energy consumption.
UR - http://www.scopus.com/inward/record.url?scp=85174960216&partnerID=8YFLogxK
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U2 - 10.1109/ICCE-Taiwan58799.2023.10227007
DO - 10.1109/ICCE-Taiwan58799.2023.10227007
M3 - Conference contribution
AN - SCOPUS:85174960216
T3 - 2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings
SP - 179
EP - 180
BT - 2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023
Y2 - 17 July 2023 through 19 July 2023
ER -